Toward machine learning interatomic potentials for modeling uranium mononitride

Alzate-Vargas, Lorena; Subedi, Kashi Nath; Lubbers, Nicholas Edward; Cooper, Michael William Donald; Tutchton, Roxanne M'liss; Gibson, Tammie Renee; Messerly, Richard Alma

doi:10.1088/2632-2153/ae0242

Toward machine learning interatomic potentials for modeling uranium mononitride

Journal Article · Wed Sep 24 00:00:00 EDT 2025 · Machine Learning: Science and Technology

DOI:https://doi.org/10.1088/2632-2153/ae0242· OSTI ID:2588095

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Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)

Uranium mononitride (UN) is a promising accident-tolerant fuel because of its high fissile density and high thermal conductivity. In this study, we developed the first machine learning interatomic potentials for reliable atomic-scale modeling of UN at finite temperatures. We constructed a training set using density functional theory (DFT) calculations that was enriched through an active learning procedure, and two neural network potentials were generated. Both potentials successfully reproduce key thermophysical properties of interest, such as temperature-dependent lattice parameter, specific heat capacity, and bulk modulus. We also evaluated the energy of stoichiometric defect reactions and defect migration barriers and found close agreement with DFT predictions, demonstrating that our potentials can be used for modeling defects in UN. Additional tests provide evidence that our potentials are reliable for simulating diffusion, noble gas impurities, and radiation damage.

View Accepted Manuscript (DOE)

Research Organization:: Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)

Sponsoring Organization:: USDOE National Nuclear Security Administration (NNSA)

Grant/Contract Number:: 89233218CNA000001

OSTI ID:: 2588095

Report Number(s):: LA-UR--24-30614; 10.1088/2632-2153/ae0242; 2632-2153

Journal Information:: Machine Learning: Science and Technology, Journal Name: Machine Learning: Science and Technology Journal Issue: 3 Vol. 6; ISSN 2632-2153

Publisher:: IOP PublishingCopyright Statement

Country of Publication:: United States

Language:: English

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